{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZtH7t1OMyz0"
      },
      "source": [
        "**DOWNLOAD THE DATASET**\n",
        "\n",
        "In this laboratory, we will use the data that are available at link https://drive.google.com/file/d/1EjvX_YGIbMe9I9zaf56dl758pG29FJET/view?usp=sharing\n",
        "\n",
        "We download the folder and add the data to the COLAB Notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qngdiwh13Ays",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94a833d7-d28b-4b88-f4d3-a6a7ae32f5ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: googledrivedownloader in /usr/local/lib/python3.11/dist-packages (1.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from googledrivedownloader) (2.32.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->googledrivedownloader) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->googledrivedownloader) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->googledrivedownloader) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->googledrivedownloader) (2025.1.31)\n",
            "Downloading 1EjvX_YGIbMe9I9zaf56dl758pG29FJET into ./ehealth_lab_1.zip... Done.\n"
          ]
        }
      ],
      "source": [
        "%pip install googledrivedownloader\n",
        "\n",
        "from googledrivedownloader import download_file_from_google_drive\n",
        "import zipfile\n",
        "\n",
        "download_file_from_google_drive(file_id='1EjvX_YGIbMe9I9zaf56dl758pG29FJET',\n",
        "                                dest_path='./ehealth_lab_1.zip',\n",
        "                                unzip=False,\n",
        "                                overwrite=True)\n",
        "\n",
        "with zipfile.ZipFile(\"./ehealth_lab_1.zip\",\"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"./ehealth_lab_1\")\n",
        "\n",
        "path_to_dataset_folder = \"./ehealth_lab_1/\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q0KqkMBcDQOR"
      },
      "source": [
        "We install and import `mne` and other useful Python packages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dQGyW-HfGZUL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d6780f4-3114-4e4c-a57c-efe3461694bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mne\n",
            "  Downloading mne-1.9.0-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.11/dist-packages (from mne) (4.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from mne) (3.1.6)\n",
            "Requirement already satisfied: lazy-loader>=0.3 in /usr/local/lib/python3.11/dist-packages (from mne) (0.4)\n",
            "Requirement already satisfied: matplotlib>=3.6 in /usr/local/lib/python3.11/dist-packages (from mne) (3.10.0)\n",
            "Requirement already satisfied: numpy<3,>=1.23 in /usr/local/lib/python3.11/dist-packages (from mne) (2.0.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from mne) (24.2)\n",
            "Requirement already satisfied: pooch>=1.5 in /usr/local/lib/python3.11/dist-packages (from mne) (1.8.2)\n",
            "Requirement already satisfied: scipy>=1.9 in /usr/local/lib/python3.11/dist-packages (from mne) (1.14.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from mne) (4.67.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->mne) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->mne) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->mne) (4.56.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->mne) (1.4.8)\n",
            "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->mne) (11.1.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->mne) (3.2.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->mne) (2.8.2)\n",
            "Requirement already satisfied: platformdirs>=2.5.0 in /usr/local/lib/python3.11/dist-packages (from pooch>=1.5->mne) (4.3.7)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.11/dist-packages (from pooch>=1.5->mne) (2.32.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->mne) (3.0.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib>=3.6->mne) (1.17.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (2025.1.31)\n",
            "Downloading mne-1.9.0-py3-none-any.whl (7.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m32.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: mne\n",
            "Successfully installed mne-1.9.0\n"
          ]
        }
      ],
      "source": [
        "%pip install mne\n",
        "import mne\n",
        "import os\n",
        "import scipy\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZA16jpPTcuoM"
      },
      "source": [
        "Today, we will work with a dataset of ECG signals from PhysioNet, a free-available repository of medical data, managed by MIT.\n",
        "\n",
        "The full dataset can be found at https://physionet.org/content/simultaneous-measurements/1.0.2/.\n",
        "\n",
        "We consider a subset of the original dataset. The data are contained in the folder \"path_to_dataset_folder\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PUa45g4tdlZ0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3f919f0-a958-4b3f-8ef9-7aae5d1839ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['x003_FAROS.edf',\n",
              " 'x001_FAROS.edf',\n",
              " 'x004_FAROS.edf',\n",
              " 'x002_FAROS.edf',\n",
              " 'x005_FAROS.edf']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "os.listdir(path_to_dataset_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-_Ddj3zlwcZ"
      },
      "source": [
        "We load one of the file that are in the folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EUMh80YGlkjo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5561dfd0-57ed-4494-f8ac-b4f7e8dabc56"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting EDF parameters from /content/ehealth_lab_1/x005_FAROS.edf...\n",
            "EDF file detected\n",
            "Setting channel info structure...\n",
            "Creating raw.info structure...\n"
          ]
        }
      ],
      "source": [
        "raw_data = mne.io.read_raw_edf(path_to_dataset_folder + 'x005_FAROS.edf')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ss0nxET9l2RK"
      },
      "source": [
        "We save some useful variables associated with the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XJbFcA9slq1o"
      },
      "outputs": [],
      "source": [
        "sampling_frequency = int(raw_data.info['sfreq']) # Sampling frequency\n",
        "sampling_period = 1 / raw_data.info['sfreq'] # Sampling period\n",
        "channel_names = raw_data.info['ch_names'] # List of channel names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-ucsiyUl2pB"
      },
      "source": [
        "We display the channel names to indentify the channel associated with the ECG recording."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ARbIKT9QlpxH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50f4ebc1-5207-4298-c68e-880a7d5586d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ECG',\n",
              " 'Accelerometer_X',\n",
              " 'Accelerometer_Y',\n",
              " 'Accelerometer_Z',\n",
              " 'Marker',\n",
              " 'HRV',\n",
              " 'DEV_Temperature']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "raw_data.info['ch_names']  # The name of the channels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V7fEAzgmmJ0W"
      },
      "source": [
        "We select a portion of the ECG signal lasting 2**12 samples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o3pQqkr0mJPy"
      },
      "outputs": [],
      "source": [
        "sample_size = 2 ** 12 # Set a power of two as sample size\n",
        "ecg_signal = raw_data.get_data()[0][:sample_size] * 1000  # The ECG is associated with the first channel, 1000 for mv conversion\n",
        "ecg_signal_domain = raw_data.times[:sample_size]  # The domain of the ECG signal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2J3iONwmWy9"
      },
      "source": [
        "We plot the ECG signal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xPacUmF_mWHM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "1e9a8dc8-9a02-4f17-c318-5feb5c2ad2ca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(ecg_signal_domain, ecg_signal)\n",
        "plt.grid()\n",
        "plt.xlim([ecg_signal_domain[0], ecg_signal_domain[-1]])\n",
        "plt.xlabel('Time [s]')\n",
        "plt.ylabel('Amplitude [mV]')\n",
        "plt.show()\n",
        "# the drop in the signal could be noise because of the respiratory"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rxann7vjEi3M"
      },
      "source": [
        "**DYADIC WAVELET TRANFORM**\n",
        "\n",
        "We consider the so-called Mexican hat as the mother wavelet."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HZuLNwYJEuEB"
      },
      "outputs": [],
      "source": [
        "def mexican_hat(x: float, mu: float = 0, sigma: float = 0.01):\n",
        "    return 2 / (np.sqrt(3 * sigma) * np.pi**0.25) * (1 - x**2 / sigma**2) * np.exp(-x**2 / (2 * sigma**2) )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMvQur8aE-uH"
      },
      "source": [
        "We plot the mother wavelet in a range centered in 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WtBn5xiFE6RE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "b58b4283-735e-4f76-9e92-33afe256719b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "mother_domain = np.linspace(-1/10, 1/10, num=int(sampling_frequency * 2 / 10), endpoint=False)\n",
        "mother_wavelet = mexican_hat(mother_domain)\n",
        "plt.plot(mother_domain, mother_wavelet)\n",
        "plt.xlim([-1/10, 1/10])\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CWUzBX4srB9Z"
      },
      "source": [
        "We define a family of dyadic wavelet atoms by scaling and shifting the mother wavelet by 2^j and m*2^j."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YXVGIK-dFiki"
      },
      "outputs": [],
      "source": [
        "def get_dyadic_wavelet_atom(mother_domain: np.ndarray,\n",
        "                            mother_wavelet: np.ndarray,\n",
        "                            j: int,\n",
        "                            m: int,\n",
        "                            sampling_period: float):\n",
        "\n",
        "  atom_scale = 2 ** j  # The scale (the parameter s in the slide set)\n",
        "  atom_shift = m * (2 ** j)  # The time shift\n",
        "\n",
        "  # These are the values of the wavelet atom\n",
        "\n",
        "  atom_wavelet = np.interp(np.arange(len(mother_wavelet) * atom_scale) / atom_scale, np.arange(len(mother_wavelet)), mother_wavelet) / np.sqrt(atom_scale)\n",
        "\n",
        "  # This is the time domain of the wavelet atom\n",
        "\n",
        "  atom_domain = atom_shift * sampling_period + np.linspace(mother_domain[0] * atom_scale, (mother_domain[-1] + sampling_period) * atom_scale, endpoint=False, num = len(mother_domain) * atom_scale)\n",
        "\n",
        "  return atom_domain, atom_wavelet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQy92c1jt4lq"
      },
      "source": [
        "We plot a sequence of wavelet atoms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KZIJTDbjswMn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "eab5b525-d194-4853-a16f-d94e28a87d24"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "j_values = [0, 1, 0, 1, 0, 1, 2, 2]   # Scale, enlarge by minimizing (higher pick but smaller support)\n",
        "m_values = [0, 0, 100, 100, 300, 300, 0, 100]   # Time Shift\n",
        "\n",
        "# The time domain of the mother wavelet\n",
        "\n",
        "mother_domain = np.linspace(-1/10, 1/10, num= int(sampling_frequency * 2 / 10))\n",
        "\n",
        "# The values of the mother wavelet\n",
        "\n",
        "mother_wavelet = mexican_hat(mother_domain)\n",
        "\n",
        "for j, m in zip(j_values, m_values):\n",
        "\n",
        "  # The atom associated with scale j and shift m\n",
        "\n",
        "  atom_domain, atom_wavelet = get_dyadic_wavelet_atom(mother_domain, mother_wavelet, j, m, sampling_period)\n",
        "\n",
        "  plt.plot(atom_domain, atom_wavelet, label = 'j,m=' + str(j) + ',' + str(m))\n",
        "\n",
        "plt.xlabel('Time [s]')\n",
        "\n",
        "plt.legend(loc=\"best\")\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGu5M_8-TzHC"
      },
      "source": [
        "We define an ad hoc method for plotting the wavelet coefficients."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KQ__DwI2T4VM"
      },
      "outputs": [],
      "source": [
        "def plot_wavelet_coefficient(wavelet_coefficient: np.ndarray,\n",
        "                             j_values: np.ndarray,\n",
        "                             m_values: np.ndarray,\n",
        "                             label='Wavelet coefficient'):\n",
        "\n",
        "  fig, ax = plt.subplots()\n",
        "\n",
        "  cax = ax.imshow(wavelet_coefficient,\n",
        "                  cmap='viridis',\n",
        "                  aspect='auto',\n",
        "                  vmin=np.percentile(wavelet_coefficient, 5),\n",
        "                  vmax=np.percentile(wavelet_coefficient, 95))\n",
        "\n",
        "  cbar = fig.colorbar(cax, label=label)\n",
        "\n",
        "  ytick_num = np.min((10, len(j_values)))\n",
        "  xtick_num = 10\n",
        "\n",
        "  ytick_size = int(len(j_values) / ytick_num)\n",
        "  yticks = [int(np.floor(ytick_size / 2)) + i * ytick_size for i in range(ytick_num)]\n",
        "  ytick_labels = [j_values[int(np.floor(ytick_size / 2)) + i * ytick_size] for i in range(ytick_num)]\n",
        "\n",
        "  xtick_size = int(len(m_values) / xtick_num)\n",
        "  xticks = [int(np.floor(xtick_size / 2)) + i * xtick_size for i in range(xtick_num)]\n",
        "  xtick_labels = [m_values[int(np.floor(xtick_size / 2)) + i * xtick_size] for i in range(xtick_num)]\n",
        "\n",
        "  plt.xticks(xticks, xtick_labels)\n",
        "  plt.yticks(yticks, ytick_labels)\n",
        "\n",
        "  plt.ylabel('j')\n",
        "  plt.xlabel('m')\n",
        "\n",
        "  plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5u2s8KPComC"
      },
      "source": [
        "We compute the wavelet transform of the signal for different scales and shift periods, i.e., different combinations of j and m."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W9xdRSNPDFOh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "ce927945-9b03-4db3-bf9c-18782f5ec6d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# The time domain of the mother wavelt\n",
        "\n",
        "mother_domain = np.linspace(-1/10, 1/10, num = int(sampling_frequency * 2 / 10), endpoint=False)\n",
        "\n",
        "# The values of the mother wavelet\n",
        "\n",
        "mother_wavelet = mexican_hat(mother_domain)\n",
        "\n",
        "j_values = np.arange(1, 6)  # Scale values\n",
        "\n",
        "m_values = np.arange(sample_size)  # Time shift values\n",
        "\n",
        "wavelet_coefficients = np.zeros((len(j_values), len(m_values)))  # Matrix where to save the wavelet coefficient\n",
        "\n",
        "for j_index, j in enumerate(j_values):\n",
        "\n",
        "  for m_index, m in enumerate(np.arange(int(sample_size / (2**j)))):\n",
        "\n",
        "    # The atom associated with scale 2 ** j and time shift m * 2 ** j\n",
        "\n",
        "    atom_domain, atom_wavelet = get_dyadic_wavelet_atom(mother_domain, mother_wavelet, j, m, sampling_period)  # Get the wavelet function associated with (j,m)\n",
        "\n",
        "    if atom_domain[0] > 0:\n",
        "\n",
        "      # We fill with zeros the portion of the signal domain that is not covered by the wavelet\n",
        "\n",
        "      atom_wavelet = np.concatenate((np.zeros(int(sampling_frequency * atom_domain[0])), atom_wavelet))\n",
        "\n",
        "    else:\n",
        "\n",
        "      # We do not consider the portion of the wavelet domain that is lower than 0\n",
        "\n",
        "      atom_wavelet = atom_wavelet[int(np.argmin(np.abs(atom_domain))):]\n",
        "\n",
        "    max_length = np.min((sample_size, len(atom_wavelet)))\n",
        "\n",
        "    # Compute the wavelet coefficient\n",
        "\n",
        "    wavelet_coefficient = np.sum(ecg_signal[:max_length] * atom_wavelet[:max_length])\n",
        "\n",
        "    wavelet_coefficients[j_index, m_index] = wavelet_coefficient\n",
        "\n",
        "plot_wavelet_coefficient(wavelet_coefficients, j_values, m_values)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "## 4096 is divided by 2 because we use j=2^1 and thats why at 1 we dont have values more than 2048 and same for the rest of them.the higher the j, the lower the m. W = N/2^j"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fFPGDe0xuCzm"
      },
      "source": [
        "Notably, we have considered a dyadic transformation where the time shift between consecutive wavelet atoms depends on the scale. This implies that we obtain a lower number of wavelet coefficients as we increase the scale."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q1l-8qi-uaDh"
      },
      "source": [
        "**DYADIC WAVELET DECOMPOSITION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HPsEs841uidv"
      },
      "source": [
        "In the following, we try to decompose and reconstruct the sinusoid signal by a set of coefficients derived from the Haar wavelet."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9lLTkHqvm5-"
      },
      "source": [
        "The Haar tarnsformation defines the following mother wavelet."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "clLUQfmHus4m"
      },
      "outputs": [],
      "source": [
        "def haar_wavelet(x):\n",
        "    h = np.zeros(len(x))\n",
        "    h[:len(x)//2] = 1\n",
        "    h[len(x)//2:] = -1\n",
        "    return h"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qu-2XDd2wpED"
      },
      "source": [
        "The Haar transformation defines the following scaling function (or father wavelet)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HtZDWHD6wtWT"
      },
      "outputs": [],
      "source": [
        "def haar_scaling_function(x):\n",
        "    h = np.ones(len(x))\n",
        "    return h"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFZxszu3xJ9z"
      },
      "source": [
        "We plot a sequence of Haar atoms by changing the scale (j) and the time shift (m)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "03nongIAkDAt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "4d07ffc8-f32c-405f-8da3-e991c76f51f5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "j_values = [0, 1, 0, 1, 0, 1, 2, 2]\n",
        "m_values = [0, 0, 500, 500, 1000, 1000, 0, 500]\n",
        "\n",
        "# The time domain of the mother wavelet\n",
        "\n",
        "mother_domain = np.linspace(-1/10, 1/10, num=int(sampling_frequency * 2 / 10), endpoint=False)\n",
        "\n",
        "# The values of the mother wavelet\n",
        "\n",
        "mother_wavelet = haar_wavelet(mother_domain)\n",
        "\n",
        "for j, m in zip(j_values, m_values):\n",
        "\n",
        "  # The wavelet atom associated with scale j and shift m\n",
        "\n",
        "  atom_domain, atom_wavelet = get_dyadic_wavelet_atom(mother_domain, mother_wavelet, j, m, sampling_period)\n",
        "\n",
        "  plt.plot(atom_domain, atom_wavelet, label=\"j,m=\"+str(j) + ',' + str(m))\n",
        "\n",
        "plt.xlabel('Time [s]')\n",
        "\n",
        "plt.legend(loc=\"best\")\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGJ7UBmLQq-k"
      },
      "source": [
        "We plot a sequence of Haar scaling functions by changing the scale (j) and the time shift (m), assuming a sampling period of 0.0001 seconds."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UudoypWlQsUY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "65bf7da2-49c7-4d8d-b0c6-33150b4c3242"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "j_values = [0, 1, 0, 1, 0, 1, 2, 2]\n",
        "m_values = [0, 0, 1000, 1000, 2000, 2000, 0, 1000]\n",
        "\n",
        "# The time domain of the father wavelet\n",
        "\n",
        "father_domain = np.linspace(-1/10, 1/10, num=int(sampling_frequency * 2 / 10), endpoint=False)\n",
        "\n",
        "# The values of the father wavelet\n",
        "\n",
        "father_wavelet = haar_scaling_function(mother_domain)\n",
        "\n",
        "for j, m in zip(j_values, m_values):\n",
        "\n",
        "  # The wavelet atom associated with scale j and shift m\n",
        "\n",
        "  atom_domain, atom_wavelet = get_dyadic_wavelet_atom(father_domain, father_wavelet, j, m, sampling_period)\n",
        "\n",
        "  plt.plot(atom_domain, atom_wavelet, label=\"j,m=\"+str(j) + ',' + str(m))\n",
        "\n",
        "plt.xlabel('Time [s]')\n",
        "\n",
        "plt.legend(loc=\"best\")\n",
        "\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IsUN9pWVxeka"
      },
      "source": [
        "We define two ad hoc methods to get the dyadic wavelet and scaling functions associated with each combination of scaling and shifting parameters (j,m).\n",
        "\n",
        "IN PARTICULAR, WE ASSUME THAT THE MOTHER WAVELET HAS A SUPPORT EQUAL TO THE SAMPLING PERIOD!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EmunIfTLuCoV"
      },
      "outputs": [],
      "source": [
        "def get_haar_dyadic_wavelet(j: int, m: int, sampling_period: float):\n",
        "\n",
        "  atom_scale = 2 ** j\n",
        "\n",
        "  atom_shift = m * (2 ** j)\n",
        "\n",
        "  # The time domain of the wavelet atom\n",
        "\n",
        "  atom_domain = np.linspace(- 2 ** (j-1), 2 ** (j-1), num=atom_scale, endpoint=False)\n",
        "\n",
        "  # The values of the wavelet atom\n",
        "\n",
        "  atom_wavelet = haar_wavelet(atom_domain)\n",
        "\n",
        "  atom_domain = atom_shift + atom_domain\n",
        "\n",
        "  return atom_domain * sampling_period, atom_wavelet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X1NQzDoIv2to"
      },
      "outputs": [],
      "source": [
        "def get_haar_dyadic_scaling_function(j: int, m: int, sampling_period: float):\n",
        "\n",
        "  atom_scale = 2 ** j\n",
        "\n",
        "  atom_shift = m * (2 ** j)\n",
        "\n",
        "  # The time domain of the wavelet atom\n",
        "\n",
        "  atom_domain = np.linspace(- 2 ** (j-1), 2 ** (j-1), num=atom_scale, endpoint=False)\n",
        "\n",
        "  # The values of the wavelet atom\n",
        "\n",
        "  atom_wavelet = haar_scaling_function(atom_domain)\n",
        "\n",
        "  atom_domain = atom_shift + atom_domain\n",
        "\n",
        "  return atom_domain * sampling_period, atom_wavelet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVxU6SrfxdsZ"
      },
      "source": [
        "We compute the coefficients associated with the wavelet and scaling functions (i.e., the detail and approximation coefficients) for each combination of parameters (j, m)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5AyYYzXuamPX"
      },
      "outputs": [],
      "source": [
        "def get_detail_coefficients(signal_length: int, j_values: np.ndarray, signal: np.ndarray, sampling_frequency: int, sampling_period: float):\n",
        "\n",
        "  detail_coefficients = np.zeros((len(j_values), int(signal_length / 2)))  # Matrix where to save the coefficients\n",
        "\n",
        "  for j_index, j in enumerate(j_values):  # Iterate for all the scale values\n",
        "\n",
        "    max_m_value = int(signal_length / (2**j))  # Max value that can be taken by m\n",
        "\n",
        "    for m_index, m in enumerate(np.arange(max_m_value)):  # Iterate for all the shift values\n",
        "\n",
        "      atom_domain, atom_wavelet = get_haar_dyadic_wavelet(j, m, sampling_period)  # Get the wavelet function associated with (j,m)\n",
        "\n",
        "      if atom_domain[0] > 0:\n",
        "\n",
        "        # We fill with zeros the portion of the signal domain that is not covered by the wavelet\n",
        "\n",
        "        atom_wavelet = np.concatenate((np.zeros(int(sampling_frequency * atom_domain[0])), atom_wavelet))\n",
        "\n",
        "      else:\n",
        "\n",
        "        # We do not consider the portion of the wavelet domain that is lower than 0\n",
        "\n",
        "        atom_wavelet = atom_wavelet[int(np.argmin(np.abs(atom_domain))):]\n",
        "        atom_domain = atom_domain[int(np.argmin(np.abs(atom_domain))):]\n",
        "\n",
        "      max_length = np.min((signal_length, len(atom_wavelet)))\n",
        "\n",
        "      # We compute the wavelet coefficient\n",
        "\n",
        "      wavelet_coefficient = np.sum(signal[:max_length] * atom_wavelet[:max_length])\n",
        "\n",
        "      detail_coefficients[j_index, m_index] = wavelet_coefficient\n",
        "\n",
        "  return detail_coefficients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uuzSvSbvxfxf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "d2628c40-08a8-456b-fb8c-f46704e63c58"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "j_values = np.arange(1, 6) # The j values must be within 1 and log(N)\n",
        "\n",
        "detail_coefficients = get_detail_coefficients(sample_size, j_values, ecg_signal, sampling_frequency, sampling_period)\n",
        "\n",
        "plot_wavelet_coefficient(detail_coefficients, j_values, np.arange(int(sample_size / 2)), label='Detail coefficient')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "## We can detect QRS the sudden increaments by looking at the very low scale (high freq, j=1)\n",
        "## in mexican we were using a larger support so thats why we couldnt detect the QRS very good but here by going to lower support and detect"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WT-CozrjbYH0"
      },
      "outputs": [],
      "source": [
        "def get_approx_coefficients(signal_length: int, j_values: np.ndarray, signal: np.ndarray, sampling_frequency: int, sampling_period: float):\n",
        "\n",
        "  approx_coefficients = np.zeros((len(j_values), int(signal_length / 2)))  # Matrix where to save the coefficients\n",
        "\n",
        "  for j_index, j in enumerate(j_values):  # Iterate for all the scale values\n",
        "\n",
        "    max_m_value = int(signal_length / (2**j))  # Max value that can be taken by m\n",
        "\n",
        "    for m_index, m in enumerate(np.arange(max_m_value)):  # Iterate for all the shift values\n",
        "\n",
        "      atom_domain, atom_scaling_function = get_haar_dyadic_scaling_function(j, m, sampling_period)  # Get the scaling function associated with (j,m)\n",
        "\n",
        "      if atom_domain[0] > 0:\n",
        "\n",
        "        # We fill with zeros the portion of the signal domain that is not covered by the wavelet\n",
        "\n",
        "        atom_scaling_function = np.concatenate((np.zeros(int(sampling_frequency * atom_domain[0])), atom_scaling_function))\n",
        "\n",
        "      else:\n",
        "\n",
        "        # We do not consider the portion of the wavelet domain that is lower than 0\n",
        "\n",
        "        atom_scaling_function = atom_scaling_function[int(np.argmin(np.abs(atom_domain))):]\n",
        "        atom_domain = atom_domain[int(np.argmin(np.abs(atom_domain))):]\n",
        "\n",
        "      max_length = np.min((signal_length, len(atom_scaling_function)))\n",
        "\n",
        "      # We compute the wavelet coefficient\n",
        "\n",
        "      wavelet_coefficient = np.sum(signal[:max_length] * atom_scaling_function[:max_length])\n",
        "\n",
        "      approx_coefficients[j_index, m_index] = wavelet_coefficient\n",
        "\n",
        "  return approx_coefficients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5HwHxVcEyJr2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "ae9c27ff-60b3-4a20-8d28-d328e316e374"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "approx_coefficients = get_approx_coefficients(sample_size, j_values, ecg_signal, sampling_frequency, sampling_period)\n",
        "\n",
        "plot_wavelet_coefficient(approx_coefficients, j_values, np.arange(int(sample_size / 2)), label='Approximation coefficient')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8xzOrLEUysBX"
      },
      "source": [
        "If we set the scale to j=1 and the signal length is N, we obtain N/2 detail coefficients (via the mother wavelet) and N/2 approximation coefficient (via the father wavelet)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GHlqHnyP-qy2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "c5fe7e7a-4f40-44d2-86ec-76bdd7243e5c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "target_j = 1\n",
        "target_j_index = np.argwhere(j_values == target_j)[0][0]\n",
        "\n",
        "plt.plot(np.arange(int(sample_size / (2**target_j))), approx_coefficients[target_j_index][:int(sample_size / (2**target_j))], c='r', label='Approx', alpha=1.0)\n",
        "plt.plot(np.arange(int(sample_size / (2**target_j))), detail_coefficients[target_j_index][:int(sample_size / (2**target_j))], c='b', label='Detail', alpha=1.0)\n",
        "plt.grid()\n",
        "plt.xlabel('m')\n",
        "plt.ylabel('Wavelet coefficient')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZH8mQUUfcMKb"
      },
      "source": [
        "If we set the scale to j=3 and the signal length is N, we obtain N/8 detail coefficients (obtained via the mother wavelet) and N/8 approximation coefficient (obtained via the father wavelet)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iHB_jGlMcKpP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "442122c5-6f48-495b-8599-4084a90a006f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "target_j = 3\n",
        "target_j_index = np.argwhere(j_values == target_j)[0][0]\n",
        "\n",
        "plt.plot(np.arange(int(sample_size / (2**target_j))), approx_coefficients[target_j_index][:int(sample_size / (2**target_j))], c='r', label='Approx', alpha=1.0)\n",
        "plt.plot(np.arange(int(sample_size / (2**target_j))), detail_coefficients[target_j_index][:int(sample_size / (2**target_j))], c='b', label='Detail', alpha=1.0)\n",
        "plt.grid()\n",
        "plt.xlabel('m')\n",
        "plt.ylabel('Wavelet coefficient')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0utS4dz4-qQV"
      },
      "source": [
        "**We** define a method to reconstruct the signal from the coefficients associated with scale j=1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Muqm8aw9CE_m"
      },
      "outputs": [],
      "source": [
        "def get_reconstructed_signal_v0(signal_length: int,\n",
        "                                detail_coefficients: np.ndarray,\n",
        "                                approx_coefficients: np.ndarray,\n",
        "                                sampling_frequency: int,\n",
        "                                sampling_period: float):\n",
        "\n",
        "  reconstructed_signal = np.zeros(signal_length)\n",
        "\n",
        "  coefficient_masks = np.zeros(int(signal_length / 2))  # Variable to monitor the number of wavelet coefficients used for the reconstruction\n",
        "\n",
        "  m_values = np.arange(int(signal_length / 2))  # Time shifts associated with scale j = 1\n",
        "\n",
        "  for sample_index in range(signal_length):\n",
        "\n",
        "    m = np.floor((sample_index - 2 ** (0)) / 2) + 1  # Select the time shift associated with the sample\n",
        "\n",
        "    atom_domain, atom_wavelet = get_haar_dyadic_wavelet(1, m, sampling_period)  # Get the wavelet function\n",
        "\n",
        "    _, atom_scaling_function = get_haar_dyadic_scaling_function(1, m, sampling_period)  # Get the scaling function\n",
        "\n",
        "    if atom_domain[0] > 0:\n",
        "\n",
        "      # We fill with zeros the portion of the signal domain that is not covered by the wavelet\n",
        "\n",
        "      atom_wavelet = np.concatenate((np.zeros(int(round(sampling_frequency * atom_domain[0]))), atom_wavelet))\n",
        "      atom_scaling_function = np.concatenate((np.zeros(int(round(sampling_frequency * atom_domain[0]))), atom_scaling_function))\n",
        "\n",
        "    else:\n",
        "\n",
        "      # We do not consider the portion of the wavelet domain that is lower than 0\n",
        "\n",
        "      atom_wavelet = atom_wavelet[int(np.argmin(np.abs(atom_domain))):]\n",
        "      atom_scaling_function = atom_scaling_function[int(np.argmin(np.abs(atom_domain))):]\n",
        "      atom_domain = atom_domain[int(np.argmin(np.abs(atom_domain))):]\n",
        "\n",
        "    if m in m_values:\n",
        "\n",
        "      m_index = np.argwhere(m_values == m)\n",
        "\n",
        "      coefficient_masks[m_index] = 2  # 1 detail and 1 approximation\n",
        "\n",
        "      # We combine the detail and the approximation coefficients, weighted by the wavelet and scaling functions, to reconstruct the signal\n",
        "\n",
        "      reconstructed_signal[sample_index] += detail_coefficients[m_index] * atom_wavelet[sample_index] / 2\n",
        "\n",
        "      reconstructed_signal[sample_index] += approx_coefficients[m_index] * atom_scaling_function[sample_index] / 2\n",
        "\n",
        "  # We observe that np.sum(coefficient_masks) is the total number of coefficients used\n",
        "  # and, thus, represents the size of the reconstructed signal\n",
        "\n",
        "  return reconstructed_signal, np.sum(coefficient_masks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WuQ_CewagkuO"
      },
      "source": [
        "We reconstruct the signal from the N/2 detail coefficients and the N/2 approximation coefficients associated with scale j=1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i9GbKp2gzTFm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c2bd036-abfa-4bc9-ccd1-c6aee3bc8f99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-f56075189b42>:44: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  reconstructed_signal[sample_index] += detail_coefficients[m_index] * atom_wavelet[sample_index] / 2\n",
            "<ipython-input-31-f56075189b42>:46: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  reconstructed_signal[sample_index] += approx_coefficients[m_index] * atom_scaling_function[sample_index] / 2\n"
          ]
        }
      ],
      "source": [
        "# We observe that detail_coefficients[0] and approx_coefficients[0] are the wavelet and approximation coefficients associated with scale j = 1\n",
        "\n",
        "reconstructed_signal, reconstruction_size = get_reconstructed_signal_v0(sample_size,\n",
        "                                                                        detail_coefficients[0],\n",
        "                                                                        approx_coefficients[0],\n",
        "                                                                        sampling_frequency,\n",
        "                                                                        sampling_period)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TwN7Hs5mg4i2"
      },
      "source": [
        "We plot the reconstructed signal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VUAnCcN3g3Ge",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "586e3cd8-88c8-492a-8cee-b6477af2fd41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(ecg_signal_domain, ecg_signal, c='b', label='Original')\n",
        "plt.plot(ecg_signal_domain, reconstructed_signal-2, c='r', label='Reconstructed')\n",
        "plt.grid()\n",
        "plt.xlabel('Time [s]')\n",
        "plt.ylabel('Signal')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPENLp-IBrob"
      },
      "source": [
        "We compute the Root Mean Squared Error (RMSE) and the compression ratio (CR) to evaluate the reconstruction performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RYQGUP4t6slV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9651a920-329f-48b0-d001-60db7b50fd42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 0.02264244191294857\n",
            "Compression Ratio (CR): 1.0\n"
          ]
        }
      ],
      "source": [
        "rmse = np.sqrt(np.mean(np.abs(reconstructed_signal - ecg_signal) ** 2))\n",
        "\n",
        "print('RMSE:', rmse)\n",
        "\n",
        "cr = reconstruction_size / sample_size\n",
        "\n",
        "print('Compression Ratio (CR):', cr)\n",
        "\n",
        "# cr is 1 becuase we have N/2 for detail and N/2 for approximation\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSKsuQ_Chs7N"
      },
      "source": [
        "We define a method to reconstruct the signal from a different combination of wavelet coefficients."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5fOurAQcC6um"
      },
      "outputs": [],
      "source": [
        "def get_reconstructed_signal_v1(signal_length: int,\n",
        "                                max_j: int,\n",
        "                                detail_coefficients: np.ndarray,\n",
        "                                approx_coefficients: np.ndarray,\n",
        "                                sampling_frequency: int,\n",
        "                                sampling_period: float):\n",
        "\n",
        "  reconstructed_signal = np.zeros(signal_length)\n",
        "\n",
        "  j_values = np.arange(1, max_j+1)  # Consider all the scale from 1 to max_j\n",
        "\n",
        "  coefficient_masks = np.zeros((len(j_values), 2, int(signal_length / 2)))  # Variable to monitor the number of wavelet coefficients used for the reconstruction\n",
        "\n",
        "  for sample_index in range(signal_length):\n",
        "\n",
        "    for j_index, j in enumerate(j_values):\n",
        "\n",
        "      m = np.floor((sample_index - 2 ** (j-1)) / (2**j)) + 1  # Select the time shift associated with the sample\n",
        "\n",
        "      atom_domain, atom_wavelet = get_haar_dyadic_wavelet(j, m, sampling_period) # Get the wavelet atom associated with (j,m)\n",
        "\n",
        "      if atom_domain[0] > 0:\n",
        "\n",
        "        # We fill with zeros the portion of the signal domain that is not covered by the wavelet\n",
        "\n",
        "        atom_wavelet = np.concatenate((np.zeros(int(round(sampling_frequency * atom_domain[0]))), atom_wavelet))\n",
        "\n",
        "      else:\n",
        "\n",
        "        # We do not consider the portion of the wavelet domain that is lower than 0\n",
        "\n",
        "        atom_wavelet = atom_wavelet[int(np.argmin(np.abs(atom_domain))):]\n",
        "\n",
        "      m_values = np.arange(int(signal_length / (2**j)))  # Time shifts associated with scale j\n",
        "\n",
        "      if m in m_values:\n",
        "\n",
        "        m_index = np.argwhere(m_values == m)\n",
        "\n",
        "        # We add the detail coefficient, weighted by the wavelet functions, to reconstruct the signal\n",
        "\n",
        "        reconstructed_signal[sample_index] += detail_coefficients[j_index, m_index] * atom_wavelet[sample_index] / (2 ** j)\n",
        "\n",
        "        coefficient_masks[j_index, 0, m_index] = 1\n",
        "\n",
        "    # Only for the max j value\n",
        "\n",
        "    if m in m_values:\n",
        "\n",
        "      m_index = np.argwhere(m_values == m)\n",
        "\n",
        "      atom_domain, atom_scaling_function = get_haar_dyadic_scaling_function(j, m, sampling_period) # Get the scaling function associated with (j,m)\n",
        "\n",
        "      if atom_domain[0] > 0:\n",
        "\n",
        "        # We fill with zeros the portion of the signal domain that is not covered by the wavelet\n",
        "\n",
        "        atom_scaling_function = np.concatenate((np.zeros(int(round(sampling_frequency * atom_domain[0]))), atom_scaling_function))\n",
        "\n",
        "      else:\n",
        "\n",
        "        # We do not consider the portion of the wavelet domain that is lower than 0\n",
        "\n",
        "        atom_scaling_function  = atom_scaling_function[int(np.argmin(np.abs(atom_domain))):]\n",
        "\n",
        "      # We add the approximation coefficient associated with the maximum scale, weighted by the scaling functions, to reconstruct the signal\n",
        "\n",
        "      reconstructed_signal[sample_index] += approx_coefficients[j_index, m_index] * atom_scaling_function[sample_index] / (2 ** j)\n",
        "\n",
        "      coefficient_masks[j_index, 1, m_index] = 1\n",
        "\n",
        "  # We observe that np.sum(coefficient_masks) is the total number of coefficients used\n",
        "  # and, thus, represents the size of the reconstructed signal\n",
        "\n",
        "  return reconstructed_signal, np.sum(coefficient_masks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2A3fjlmtzwp4"
      },
      "source": [
        "We reconstruct the signal from the wavelet coefficient associated with scale j=1,2,3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Jf_X5KshxNQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c850486-095e-458d-9eb6-4e8f709d1fd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-3a5ecda0a648>:42: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  reconstructed_signal[sample_index] += detail_coefficients[j_index, m_index] * atom_wavelet[sample_index] / (2 ** j)\n",
            "<ipython-input-31-3a5ecda0a648>:68: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  reconstructed_signal[sample_index] += approx_coefficients[j_index, m_index] * atom_scaling_function[sample_index] / (2 ** j)\n"
          ]
        }
      ],
      "source": [
        "max_j = 3\n",
        "\n",
        "reconstructed_signal, coefficient_number = get_reconstructed_signal_v1(sample_size,\n",
        "                                                                       max_j,\n",
        "                                                                       detail_coefficients,\n",
        "                                                                       approx_coefficients,\n",
        "                                                                       sampling_frequency,\n",
        "                                                                       sampling_period)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3NY1cJMik6R"
      },
      "source": [
        "We plot the reconstructed signal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_WKZ95l3ilEZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "569f6634-101c-42c4-9ab3-fba56d8f57ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(ecg_signal_domain, ecg_signal, c='b', label='Original')\n",
        "plt.plot(ecg_signal_domain, reconstructed_signal-2, c='r', label='Reconstructed')\n",
        "plt.grid()\n",
        "plt.xlabel('Time [s]')\n",
        "plt.ylabel('Signal')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwRwPa-kzHVC"
      },
      "source": [
        "We compute the Root Mean Squared Error (RMSE) and the compression ratio (CR) to evaluate the reconstruction performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eSA7WVXA6woA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9465d438-da0a-47e3-f40f-a93b60410735"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 0.07634668003101257\n",
            "Compression Ratio (CR): 1.0\n"
          ]
        }
      ],
      "source": [
        "rmse = np.sqrt(np.mean(np.abs(reconstructed_signal - ecg_signal) ** 2))\n",
        "\n",
        "print('RMSE:', rmse)\n",
        "\n",
        "cr = coefficient_number / sample_size\n",
        "\n",
        "print('Compression Ratio (CR):', cr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Gk3TV30B9Tn"
      },
      "source": [
        "We observe that the increase of the maximum scale does not improve the quality of the reconstruction: indeed, both the above methods use the same number of wavelet coefficients for reconstructing the target signal, which is equal to the number of signal samples. If our goal is to compress the signal and save bandwidth, the above approach is meaningless.\n",
        "\n",
        "To take advantage of the Wavelet Transform (WT), we should reduce the number of information used for the signal reconstruction, prioritizing those coefficients that are more significant."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oyGpEMgFL5M"
      },
      "source": [
        "**YOUR TURN**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hFG51dpFnlW"
      },
      "source": [
        "In the following, you are asked to define a new method (named `get_reconstructed_signal_v2`) to encode and reconstruct a signal from its wavelet coefficients, using not more than N/4 samples with respect to the initial signal representation. In other words, you are asked to reduce the bandwidth by four times. To test your method, you must consider the ECG signals contained in the laboratory folder.\n",
        "\n",
        "Notably, a trivial solution is to exploit only N/8 detail and N/8 approximation coefficients associated with a specific scale. A smarter solution may involve the use of multiple coefficients from various scales. To do so, you can modify the method `get_reconstructed_signal_v1`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-_jLoS7dtS3"
      },
      "source": [
        "**HOW TO COMPLETE THE LAB**\n",
        "\n",
        "To complete the laboratory, you must test your method in each of the ECG signals included in the laboratory folder, and compute, for each signal, the performance in terms of Root Mean Squared Error (RMSE) and Compression Ratio (CR).\n",
        "\n",
        "The results, including your method's script and the performance (RMSE and CR) associated with each signal, must be included in a report of a maximum of three pages.\n",
        "\n",
        "In the laboratory report, you must discuss the reasons behind your methodology and its limitations and benefits in relation with the results."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_reconstructed_signal_v2(signal_length: int,\n",
        "                                max_j: int,\n",
        "                                ecg_signal: np.ndarray,\n",
        "                                detail_coefficients: np.ndarray,\n",
        "                                approx_coefficients: np.ndarray,\n",
        "                                sampling_frequency: int,\n",
        "                                sampling_period: float,\n",
        "                                num_coefficients_to_keep: int):\n",
        "\n",
        "    # Initialize reconstruction and residual.\n",
        "    reconstruction = np.zeros(signal_length)\n",
        "    residual = ecg_signal.copy()\n",
        "\n",
        "    candidate_list = []\n",
        "    # Build candidate dictionary over scales and shifts.\n",
        "    for j in range(1, max_j + 1):\n",
        "        m_max = int(signal_length / (2 ** j))\n",
        "        for m in range(m_max):\n",
        "            # Compute proper shift: dyadic translation.\n",
        "            shift_index = m * (2 ** j)\n",
        "            # Candidate from detail coefficients:\n",
        "            coeff_val = detail_coefficients[j - 1, m]\n",
        "            atom_domain, atom_wavelet = get_haar_dyadic_wavelet(j, m, sampling_period)\n",
        "            # Embed the atom at the correct time index.\n",
        "            atom_vector = np.zeros(signal_length)\n",
        "            L = len(atom_wavelet)\n",
        "            if shift_index + L > signal_length:\n",
        "                L = signal_length - shift_index  # truncate if needed\n",
        "            atom_vector[shift_index:shift_index + L] = atom_wavelet[:L]\n",
        "            candidate_list.append({\n",
        "                'type': 'detail',\n",
        "                'j': j,\n",
        "                'm': m,\n",
        "                'atom': atom_vector,\n",
        "                'used': False\n",
        "            })\n",
        "\n",
        "            # Candidate from approximation coefficients:\n",
        "            coeff_val = approx_coefficients[j - 1, m]\n",
        "            atom_domain, atom_scaling = get_haar_dyadic_scaling_function(j, m, sampling_period)\n",
        "            atom_vector = np.zeros(signal_length)\n",
        "            L = len(atom_scaling)\n",
        "            if shift_index + L > signal_length:\n",
        "                L = signal_length - shift_index\n",
        "            atom_vector[shift_index:shift_index + L] = atom_scaling[:L]\n",
        "            candidate_list.append({\n",
        "                'type': 'approx',\n",
        "                'j': j,\n",
        "                'm': m,\n",
        "                'atom': atom_vector,\n",
        "                'used': False\n",
        "            })\n",
        "\n",
        "    used_candidates = []\n",
        "\n",
        "    # Matching Pursuit Loop:\n",
        "    for iter_idx in range(num_coefficients_to_keep):\n",
        "        best_ip = 0\n",
        "        best_candidate = None\n",
        "        # Search through unused candidates:\n",
        "        for candidate in candidate_list:\n",
        "            if candidate['used']:\n",
        "                continue\n",
        "            ip = np.dot(residual, candidate['atom'])\n",
        "            if abs(ip) > abs(best_ip):\n",
        "                best_ip = ip\n",
        "                best_candidate = candidate\n",
        "        if best_candidate is None:\n",
        "            break  # No candidate found (should not happen)\n",
        "\n",
        "        # Compute the optimal coefficient:\n",
        "        norm_sq = np.dot(best_candidate['atom'], best_candidate['atom'])\n",
        "        if norm_sq == 0:\n",
        "            coeff = 0.0\n",
        "        else:\n",
        "            coeff = best_ip / norm_sq\n",
        "\n",
        "        # Update reconstruction:\n",
        "        reconstruction += coeff * best_candidate['atom']\n",
        "        best_candidate['used'] = True\n",
        "        used_candidates.append((best_candidate, coeff))\n",
        "\n",
        "        # Update residual:\n",
        "        residual = ecg_signal - reconstruction\n",
        "\n",
        "    coefficient_count = len(used_candidates)\n",
        "    return reconstruction, coefficient_count\n",
        "\n",
        "# === Testing the improved version ===\n",
        "# Set parameters as before:\n",
        "max_j = 5\n",
        "num_coefficients_to_keep = int(sample_size / 4)\n",
        "\n",
        "reconstructed_signal, coefficient_number = get_reconstructed_signal_v6(\n",
        "    sample_size,\n",
        "    max_j,\n",
        "    ecg_signal,\n",
        "    detail_coefficients,\n",
        "    approx_coefficients,\n",
        "    sampling_frequency,\n",
        "    sampling_period,\n",
        "    num_coefficients_to_keep\n",
        ")\n",
        "\n",
        "rmse = np.sqrt(np.mean((reconstructed_signal - ecg_signal)**2))\n",
        "cr = coefficient_number / sample_size\n",
        "print('RMSE with matching pursuit and proper shifting:', rmse)\n",
        "print('Compression Ratio (CR):', cr)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJF9WU62R6Zv",
        "outputId": "0dcd7a9b-7cc3-4ff2-9963-302c1693e28e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE with matching pursuit and proper shifting: 0.009070904642600772\n",
            "Compression Ratio (CR): 0.25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(ecg_signal_domain, ecg_signal, c='b', label='Original')\n",
        "plt.plot(ecg_signal_domain, reconstructed_signal-2, c='r', label='Reconstructed')\n",
        "plt.grid()\n",
        "plt.xlabel('Time [s]')\n",
        "plt.ylabel('Signal')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Y3jhsncjR6WX",
        "outputId": "0a1b8d3d-9187-4c1e-9357-267c7212da19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}